I build and deploy AI systems inside companies — turning ideas into real workflows that actually get used.
Most teams don’t fail because of AI tools, they fail because they don’t have the right struc
Location
Wylie, TX
Work
Managing Director at Colaberry — focused on AI training and enterprise deployment
Building a production-grade voice AI agent in under 8 weeks is an impressive feat. In our accelerator program, we've observed that the key to such rapid deployment often lies in leveraging robust frameworks and pre-existing platforms like Twilio and Anthropic Claude, as you did. This approach allows teams to focus more on customization and integration rather than building from scratch. One technical insight worth considering is the use of a modular architecture. Designing your AI system with interchangeable components can significantly speed up development and testing phases. For instance, you can isolate your natural language understanding (NLU) component from the dialogue management logic, allowing you to iterate on each independently. This separation also aids in quickly adapting to new requirements or scaling your system as user demands grow. Another practical tactic is implementing continuous integration and deployment (CI/CD) pipelines early in the project. This ensures that every code change is automatically tested and deployed, reducing the risk of last-minute integration issues. Tools like Jenkins or GitHub Actions can automate these processes efficiently. Finally, user feedback loops are crucial. Regularly testing your AI agent with real users and incorporating their feedback can vastly improve the conversational flow and user satisfaction. This iterative approach ensures that the final product is both functional and user-friendly. For those looking to replicate suc
Building AI agents & voice assistants at Autor (autor.ca) · Made Loquent — production voice AI for healthcare · Toronto · Anthropic, OpenAI, Twilio, LangChain
Thanks Ali — modular architecture was actually something we refactored into at week 6, after realizing how tightly coupled our telephony and conversation layers were. Separating the conversation engine from the Twilio plumbing made testing dramatically easier and let us swap components independently.
On CI/CD: we had pipelines early, but the hard part was building a meaningful test suite for voice interactions. You can't really unit-test "does this feel natural?" — we ended up with functional tests covering the tool-calling layer plus structured manual call reviews with real audio.
Curious what you mean by "accelerator program" — are you working on voice AI specifically, or more general agent deployments? We've found healthcare introduces some interesting constraints around what agents can and can't handle that end up shaping the architecture quite a bit.
For further actions, you may consider blocking this person and/or reporting abuse
We're a place where coders share, stay up-to-date and grow their careers.
Building a production-grade voice AI agent in under 8 weeks is an impressive feat. In our accelerator program, we've observed that the key to such rapid deployment often lies in leveraging robust frameworks and pre-existing platforms like Twilio and Anthropic Claude, as you did. This approach allows teams to focus more on customization and integration rather than building from scratch. One technical insight worth considering is the use of a modular architecture. Designing your AI system with interchangeable components can significantly speed up development and testing phases. For instance, you can isolate your natural language understanding (NLU) component from the dialogue management logic, allowing you to iterate on each independently. This separation also aids in quickly adapting to new requirements or scaling your system as user demands grow. Another practical tactic is implementing continuous integration and deployment (CI/CD) pipelines early in the project. This ensures that every code change is automatically tested and deployed, reducing the risk of last-minute integration issues. Tools like Jenkins or GitHub Actions can automate these processes efficiently. Finally, user feedback loops are crucial. Regularly testing your AI agent with real users and incorporating their feedback can vastly improve the conversational flow and user satisfaction. This iterative approach ensures that the final product is both functional and user-friendly. For those looking to replicate suc
Thanks Ali — modular architecture was actually something we refactored into at week 6, after realizing how tightly coupled our telephony and conversation layers were. Separating the conversation engine from the Twilio plumbing made testing dramatically easier and let us swap components independently.
On CI/CD: we had pipelines early, but the hard part was building a meaningful test suite for voice interactions. You can't really unit-test "does this feel natural?" — we ended up with functional tests covering the tool-calling layer plus structured manual call reviews with real audio.
Curious what you mean by "accelerator program" — are you working on voice AI specifically, or more general agent deployments? We've found healthcare introduces some interesting constraints around what agents can and can't handle that end up shaping the architecture quite a bit.